Passing Large Number of Arguments as a Dictionary

Let's take example of plot_bands. It requires 10 arguments and most of them are default, but in order to tweak parameters, you still need to access them. Follow These steps to input arguments easily.
In code cell, write plot_bands? and hit enter. This will give Signature and DocString.
Copy arguments and pass to a dictionary dict(what you copied). In a Jupyter Notebook cell, you can edit it:

arg_dict=dict(
    ax=None,
    kpath=None,
    bands=None,
    showlegend=True,
    E_Fermi=0,
    color1=(1, 0, 0.8),
    style1='solid',
    lw1=0.7,
    )
arg_dict

{'ax': None,
 'kpath': None,
 'bands': None,
 'showlegend': True,
 'E_Fermi': 0,
 'color1': (1, 0, 0.8),
 'style1': 'solid',
 'lw1': 0.7}

As you can see, I deleted few unnecessary arguments. Now you can use dictionary unpacking operator ** inside function, it will pass all arguments present in dictionary. Make sure you do not change name of variables, although you can delete as few of them.
Usage: Call function as plot_bands(**arg_dict). You can edit dictionary on fly, or can save it to a file to read (Not recommended, could be a threat to your system security as reading dictionaries from a file could run potentially harmful commands as well.)

Simple Band Struture Plots

Working in object-oriented way, we can have plenty of options in matplotlib. See the example below, which provides an overview of flexibility of matplotlib. All functions are defined in object-oriented way for better compatibility and flexibility.

Example: Graphene

import pivotpy.vr_parser as vp
vr1=vp.export_vasprun(path=f1)
vr2=vp.export_vasprun(path=f2)
import pivotpy.s_plots as sp
import matplotlib.pyplot as plt

fig,ax=plt.subplots(1,3,figsize=(10.2,2.6),sharey=True)
ax0=sp.plot_bands(ax=ax[0],kpath=vr2.kpath,bands=vr2.bands,showlegend=True)

ax1=sp.plot_bands(ax=ax[1],kpath=vr1.kpath,bands=vr1.bands,color1='y')

ax2=sp.plot_bands(ax=ax[2],kpath=vr1.kpath,bands=vr1.bands,color1='y')
ax2=sp.plot_bands(ax=ax[2],kpath=vr2.kpath,bands=vr2.bands,showlegend=True)
xticks=[vr1.kpath[i] for i in [0,30,60,-1]]
txts=["Polarized","Unpolarized","Comparison"]
for axes,txt in zip(ax,txts):
    if axes==ax0:
        sp.modify_axes(ax=axes,ylabel='Energy (eV)')
    sp.modify_axes(ax=axes,ylim=[-10,10],xlim=[xticks[0],xticks[-1]],xticks=xticks,xt_labels=[r'$\Gamma$','M','K',r'$\Gamma$'])
    axes.text(0.05,0.9,txt,bbox=dict(edgecolor='white',facecolor='white', alpha=0.9),transform=axes.transAxes,color='red')
plt.subplots_adjust(hspace=0.01,wspace=0.05)

Below is example where you can add multiple text entries on a quick_bplot.

ax=sp.quick_bplot(path_evr=f2,elim=[-5,5],xt_indices=[0,30,60,-1],xt_labels=['W','K',''],txt='Graphene',ctxt='r')
sp.add_text(ax=ax,xs=[0.35,0.5],ys=[0.55,0.7],txts=[r'$E_{gap}$ = 0 eV','edge states'],colors=['red','blue'])

import pivotpy.s_plots as sp
import matplotlib.pyplot as plt 
plt.style.use('dark_background')
fig,ax=plt.subplots(1,2,figsize=(9,1))
add_colorbar(ax=ax[0],vertical=True,N=7)
add_colorbar(ax=ax[1],scales=[0.9,0.9,0.9],N=21)

Including Atomic and Orbital Projections

Using `create_rgb_line` in object oriented way.

The below example let's user know how to connect pieces of objects to create a complex figure. Note that in case of adding multiple collections on an axes,(left figure),what you see depends on what was the order of adding collections on each other. Also multiple lines are automatically normalized to real data if they have same max_width, so you can compare, say, s line and p line with each other.

import time
start=time.time()
import numpy as np 
import pivotpy.vr_parser as vp
path='E:/Research/graphene_example/ISPIN_2/bands/vasprun.xml'
vr=vp.export_vasprun(path=path,elim=[-10,10])
k=vr.kpath
ef=vr.tdos.E_Fermi
en=vr.bands.evals.SpinUp-ef
pros=vr.pro_bands.pros.SpinUp

Loading from PowerShell Exported Data...

import pivotpy.s_plots as sp 
import matplotlib.pyplot as plt 
fig1,ax = plt.subplots(1,3,figsize=(10.2,2.6),sharey=True)
axc     = fig1.add_axes([0.5,1,0.3,0.04])
C1      = range(0,1)
spd     = [[0],[1],[2,3]]
sp.create_rgb_lines(ax=ax[0],kpath=k,evals_set=en,pros_set=pros,interpolate=True,n=15,uni_color=True,orbs=[[0],[0],[0]],ions=C1)
sp.create_rgb_lines(ax=ax[0],kpath=k,evals_set=en,pros_set=pros,\
        uni_color=True,color=(0,1,0,1),orbs=[[],[1],[]],ions=C1)
sp.create_rgb_lines(ax=ax[0],kpath=k,evals_set=en,pros_set=pros,uni_color=True,color=(0,0,1,0.4),orbs=[[],[],[2,3]],ions=C1)
sp.create_rgb_lines(ax=ax[1],kpath=k,evals_set=en,pros_set=pros,orbs=spd,ions=C1)
sp.create_rgb_lines(ax=ax[2],kpath=k,evals_set=en,pros_set=pros,\
        uni_width=True,orbs=spd,ions=C1)
[sp.modify_axes(ax=axes) for axes in ax]
sp.add_legend(ax=ax[0],labels=['C-s','C-pz','C-px+py'],widths=4)
sp.add_colorbar(ax=axc,colors=[(1,0,0),(0,1,0),(0,0,1)],ticklabels=['C-s','C-pz','C-px+py'],ticks=[0,0.5,1])
plt.subplots_adjust(wspace=0.05)
txts=['uni_color=True','variable width/color','uni_width=True']
[sp.add_text(ax=axes,txts=txt) for axes,txt in zip(ax,txts)];
print('Executed in {} seconds.'.format(time.time()-start))

No handles with labels found to put in legend.
Executed in 49.004796504974365 seconds.

quick_rgb_lines() is waraper around create_rgb_lines(uni_color=False). You can pass lists for orbs,labels,colors and elements eeach of length 3 with one axis.
- If you do not provide any arguemnts, this will create graph of whole system projections over s,p,d orbitals.
- elements argument is special, you can pass index of element which will pick all ions of that type, or list(length=3) of indices of ions, e.g in elements=[0,[0,1],2] of system Ga32As31Bi1, 0 and 2 pick all ions of Ga and Bi respectively, while [0,1] will pick first two ions of Ga.
- If scale_color=True, ecah point on plot is scaled to maximum color, if False, whole plot is scaled.
- color_matrix is to convert between color spaces and play around as you can. Sum of each row should be less than or equal to 1. Only works if scale_color=True. {% include tip.html content='Gray scale matrix = pivotpy.gray_matrix and kind of body centered cubic matrix = pivotpy.color_matrix.' %}

import os
import numpy as np
os.chdir('E:/Research/graphene_example/ISPIN_2/bands')
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pivotpy as pp
labels=['s','p$_z$','p$_x$+p$_y$']
axs=pp.init_figure(nrows=2,ncols=3,figsize=(9,5),sharex=True,sharey=True);
quick_rgb_lines(ax=axs[0,0],elements=[0,[0,1],[0,1]],orbs=[0,[1],[2,3]],spin='up',txt='scale_color=False',colorbar=True,labels=labels,scale_color=False)
quick_rgb_lines(ax=axs[0,1],elements=[0,[0,1],[0,1]],orbs=[0,[1],[2,3]],spin='down',txt='Intensity reduced',colorbar=True,labels=labels,color_matrix=np.diag([0.5,0.7,0.5]))
quick_rgb_lines(ax=axs[0,2],elements=[0,[0,1],[0,1]],orbs=[0,[1],[2,3]],spin='both',txt='scale_color=True',colorbar=True,labels=labels)

plt.style.use(['default','seaborn'])
_ = quick_rgb_lines(ax=axs[1,0],elements=[0,[0,1],[0,1]],orbs=[0,[2],[1,3]],spin='up',txt='color_matrix',colorbar=True,labels=labels,color_matrix=pp.color_matrix)
_ = quick_rgb_lines(ax=axs[1,1],elements=[0,[0,1],[0,1]],orbs=[0,[2],[1,3]],spin='down',txt='color_matrix.T',colorbar=True,labels=labels,color_matrix=pp.color_matrix.T)
_ = quick_rgb_lines(ax=axs[1,2],elements=[0,[0,1],[0,1]],orbs=[0,[2],[1,3]],spin='both',txt='gray_matrix',colorbar=True,labels=labels,color_matrix=[[0.3  , 0.59  , 0.11],[0.3  , 0.59  , 0.11],[0.3  , 0.59  , 0.11]])

See below the colormap RGB_m represents the last axes of the above figure. This is useful to dirctly use in DOS plot to get same colors as bands.

plt.style.use(['default'])
x = np.linspace(0,1,256)
x = np.vstack((x,x))
x=plt.cm.get_cmap('RGB_m')(x)
plt.imshow(x)

<matplotlib.image.AxesImage at 0x25ef5483648>

quick_color_lines() is waraper around create_rgb_lines(uni_color=True,scale_color=False). You can pass equal length lists for orbs,labels, and elements either with one axis or mutltiple axes.
- If you do not provide any arguemnts, this will plots-orbital of first ion.
- elements argument is special, you can pass index of element which will pick all ions of that type, or list(length=3) of indices of ions, e.g in elements=[0,[0,1],2] of system Ga32As31Bi1, 0 and 2 pick all ions of Ga and Bi respectively, while [0,1] will pick first two ions of Ga.
- If your given len(axis)=1, all projections are plotted on single axis and you can tweak opacity, legend display etc. There are plenty of options.

Colors Selection

Instead of giving custom colors, you can use matplotlib's colormaps to be consistent. Use

plt.colormaps()

to see list of available color maps. To get a color array from a map, you can do the following:

from matplotlib.pyplot import cm
colors  = cm.hsv(np.linspace(0,1,3))
# This will give you three colors from 'hsv' map.

Note: Two custom colormaps RGB,RGB_m are registered in session when you import pivotpy,which could be used when plotting DOS with bands of same color. RGB_m at any moment would represent the latest quick_rgb_lines.

import os
path='E:/Research/graphene_example/ISPIN_2/bands'
os.chdir(path)
import pivotpy as pp
import matplotlib.pyplot as plt
axs=pp.init_figure(nrows=1,ncols=3,figsize=(7,2.5),sharey=True,sharex=True)
args_dict=dict(elements=[0,0,[0,1]],orbs=[0,1,[2]],labels=['s','$p_z$','$p_x$'],hspace=0.1,wspace=0.07,showlegend=True)
quick_color_lines(axes=axs[0],**args_dict,left=0.06,color_map='flag',spin='up');
quick_color_lines(axes=axs[1],**args_dict,left=0.06,color_map='FCC',spin='down');
quick_color_lines(axes=axs[2],**args_dict,left=0.06,color_map='RGB',spin='both');

Initializing Figure

Tweaking `init_figure` by using `gridspec`.

This is a powerful way yo include any type of grid. For this, first use gs = axs[0,0].get_gridspec() and then remove axes you want to replace for another shape, then add required axis by plt.gcf().add_subplot(gs[x_ind, y_ind]). This process is illustrated in below examples.

Soft Tweaking
- Axes remain same, just widths and height ratios are chnaged.

import pivotpy.s_plots as sp 
axs=sp.init_figure(figsize=(3.4,2.6),ncols=3,widths=[3.4,1,3.4],nrows=3,heights=[2.6,1,2.6],wspace=0.076,hspace=0.1)
[[sp.modify_axes(ax=a,xticks=[0],yticks=[0]) for a in ax] for ax in axs]
[sp.add_colorbar(ax=ax,ticks=[]) for ax in [axs[1,0],axs[1,2]]];
[sp.add_colorbar(ax=ax,vertical=True,ticks=[]) for ax in [axs[0,1],axs[2,1]]];

Brute Force Tweaking
- Here we will remove and regenerate axes based on our grid choice.

axs=sp.init_figure(figsize=(5,3.4),nrows=3,ncols=2,widths=[1,1],heights=[1,7,7],wspace=0.4,hspace=0.4,axes_off=[(2,0)],sharex=True,sharey=True)
import pivotpy.s_plots as sp 
import matplotlib.pyplot as plt
gs = axs[0,0].get_gridspec()
axs_to_remove=[*axs[0, :],*axs[1:, 1]]
for ax in axs_to_remove:
    ax.remove()
axlarge = plt.gcf().add_subplot(gs[0, :])
axv = plt.gcf().add_subplot(gs[1:, 1])
sp.modify_axes(ax=axv)
sp.add_colorbar(ax=axlarge)
sp.add_text(ax=axs[1,0],txts='axis[1,0]',xs=0.25,ys=0.5)
sp.add_text(ax=axs[2,0],txts='axis[2,0] is off',xs=0.15,ys=0.5)
import pivotpy.vr_parser as vp 
vr=vp.export_vasprun(path='E:/Research/graphene_example/ISPIN_1/bands/vasprun.xml')
sp.quick_bplot(path_evr=vr,ax=axv,txt='Plotting',E_Fermi=10)
sp.add_text(ax=axv,txts='BigAxes',xs=0.25,ys=0.5)

Mixing 2D and 3D axes in a figure
- A minial example below shows deleting an axes and then adding a 3D axes with same dimensions.

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt 
import pivotpy as pp 
ax = pp.init_figure(nrows=2,ncols=2,widths=[1,2],heights=[1,2])
pos = ax[1,1].get_position()
ax[1,1].remove()
ax[1,1]=plt.gcf().add_axes(pos,projection='3d')

Plotting Density of States

Providing labels while using collect_dos is important, it will automatically return spin up/down saymbols.

quick_dos_lines(path_evr='E:/Research/graphene_example/ISPIN_2/dos/vasprun.xml',vertical=False,fill_area=True,showlegend=True,include_dos='pdos',orbs=[[1,2,3],0,1],elements=[0,0,1],linewidth=1,labels=['p','s','g'],color_map='RGB',elim=[-5,5],spin='both')

<matplotlib.axes._subplots.AxesSubplot at 0x177114e8088>

High Display Image in Notebook

The function below plt_to_html is implemented for use in pivotpy-dash app to view and save SVG image directly from web app's interface. This also enables high display output in jupyter notebook.

import pivotpy as pp 
import matplotlib.pyplot as plt
pp.quick_bplot("E:/Research/graphene_example/ISPIN_1/bands/vasprun.xml",elim=[-9,9]);
fig = plt_to_html(dash_html=None,transparent=False)
fig

Below code snippest could be used to inclue svg in html document from a figure.

data = plt_to_html(dash_html=False)
html_str= """
<!DOCTYPE html>
<head></head>
<body>
    <div>
    {}
    </div>
</body>
""".format(data)

with open('fig.html','w') as f:
    f.write(html_str)
f.close()

Passing Large Number of Arguments as a Dictionary

Simple Band Struture Plots

`plot_bands`[source]

`modify_axes`[source]

Example: Graphene

`quick_bplot`[source]

`add_text`[source]

`add_legend`[source]

`add_colorbar`[source]

Including Atomic and Orbital Projections

`create_rgb_lines`[source]

Using `create_rgb_line` in object oriented way.

`quick_rgb_lines`[source]

`quick_color_lines`[source]

Colors Selection

Initializing Figure

`init_figure`[source]

Tweaking `init_figure` by using `gridspec`.

Plotting Density of States

`select_pdos`[source]

`collect_dos`[source]

`quick_dos_lines`[source]

High Display Image in Notebook

`plt_to_html`[source]

Passing Large Number of Arguments as a Dictionary

Simple Band Struture Plots

plot_bands[source]

modify_axes[source]

Example: Graphene

quick_bplot[source]

add_text[source]

add_legend[source]

add_colorbar[source]

Including Atomic and Orbital Projections

create_rgb_lines[source]

Using create_rgb_line in object oriented way.

quick_rgb_lines[source]

quick_color_lines[source]

Colors Selection

Initializing Figure

init_figure[source]

Tweaking init_figure by using gridspec.

Plotting Density of States

select_pdos[source]

collect_dos[source]

quick_dos_lines[source]

High Display Image in Notebook

plt_to_html[source]

`plot_bands`[source]

`modify_axes`[source]

`quick_bplot`[source]

`add_text`[source]

`add_legend`[source]

`add_colorbar`[source]

`create_rgb_lines`[source]

Using `create_rgb_line` in object oriented way.

`quick_rgb_lines`[source]

`quick_color_lines`[source]

`init_figure`[source]

Tweaking `init_figure` by using `gridspec`.

`select_pdos`[source]

`collect_dos`[source]

`quick_dos_lines`[source]

`plt_to_html`[source]